Internal combustion engines may experience power loss due to, for example, clogged fuel injectors or misfiring cylinders. These power loss conditions may cause excess exhaust emissions. In order to comply with government regulation of exhaust emissions, manufacturers of internal combustion engines may provide systems for monitoring engine performance. Knowledge of crankshaft torque may provide an indication of both an actual power level and the presence of a power loss condition of the engine. However, direct measurement of crankshaft torque requires an expensive measuring device, such as a dynamometer. Further, the accurate estimation of crankshaft torque requires complex calculations and input from a variety of engine sensors.
Prior art methods for simplifying torque calculations have been developed. One such method is described in U.S. Pat. No. 6,234,010 to Zavarehi et al. In this method, the crankshaft speed and torque output of a test engine are measured and a pattern matching algorithm, such as a radial basis neural network or other neural network, is used to create a model that correlates small fluctuations in crankshaft speed with variations in crankshaft kinetic energy caused by the firing and compression events of each cylinder. The model may then be used to estimate crankshaft torque based upon crankshaft speed fluctuations measured during operation.
However, small variations between engines can introduce large variations in instantaneous crankshaft speed. For example, slight differences in crankshaft tolerances or materials may affect the inertia of the crankshaft, and thus affect instantaneous crankshaft speed. Consequently, the Zavarehi et al. model may produce an unacceptably high margin of error when applied to engines other than the test engine that it was developed on. Even where the model is applied to the test engine, the margin of error may become unacceptable as the test engine's crankshaft wears over time.
Further, the process of constructing the neural network model is time consuming and expensive. Consequently, it is impractical to construct such a model for each engine produced, or to reconstruct the model for the test engine when it becomes inaccurate.
The presently disclosed systems and methods for calibrating models of internal combustion engine performance are directed to solving one or more of these shortcomings of the prior art systems and methods.